“Carotenoid analysis of cassava genotypes roots (Manihot esculenta Crantz) cultivated in southern Brazil using chemometric tools” Author: “Moresco, R.(2014)” Date: “Thursday, January 22, 2015”

R script for Analysis of HPLC and UV-Visible Spectrophotometric Data

Reading data and metadata

setwd("/Users/Windows/Desktop/Miguel/Metabolomics-package")
source("http://bioconductor.org/biocLite.R")
source("scripts/init.R")
uv.cassava.metadata.file = "Datasets/CassavaCultivars/UVV/metadata/cass_uv_metadata.csv"
uv.cassava.data.file = "Datasets/CassavaCultivars/UVV/data/uvv-cassava.csv"

label.x = "wavelength(nm)"
label.val = "absorbance"
uv.cassava.ds = read.dataset.csv(uv.cassava.data.file, uv.cassava.metadata.file, 
                           description = "UV data for cassava", type = "uvv-spectra", format = "col",
                                 label.x = label.x, label.values = label.val)

Preliminary Inspection of Data

sum.dataset(uv.cassava.ds)
## Dataset summary:
## Valid dataset
## Description:  UV data for cassava 
## Type of data:  uvv-spectra 
## Number of samples:  30 
## Number of data points 501 
## Number of metadata variables:  3 
## Label of x-axis values:  wavelength(nm) 
## Label of data points:  absorbance 
## Number of missing values in data:  0 
## Mean of data values:  0.1605 
## Median of data values:  0.01487 
## Standard deviation:  0.4252 
## Range of values:  -0.1068 2.722 
## Quantiles: 
##         0%        25%        50%        75%       100% 
## -0.1067503 -0.0005422  0.0148732  0.0888727  2.7218540

Get metadata

get.metadata(uv.cassava.ds)
##                   varieties colors replicates
## Apronta mesa_1 Apronta.mesa  cream          1
## Apronta mesa_2 Apronta.mesa  cream          2
## Apronta mesa_3 Apronta.mesa  cream          3
## Pioneira_1         Pioneira yellow          1
## Pioneira_2         Pioneira yellow          2
## Pioneira_3         Pioneira yellow          3
## Oriental_1         Oriental  cream          1
## Oriental_2         Oriental  cream          2
## Oriental_3         Oriental  cream          3
## Amarela_1           Amarela yellow          1
## Amarela_2           Amarela yellow          2
## Amarela_3           Amarela yellow          3
## Catarina_1         Catarina yellow          1
## Catarina_2         Catarina yellow          2
## Catarina_3         Catarina yellow          3
## IAC 576-70_1     IAC.576.70 yellow          1
## IAC 576-70_2     IAC.576.70 yellow          2
## IAC 576-70_3     IAC.576.70 yellow          3
## Salezio_1           Salezio  cream          1
## Salezio_2           Salezio  cream          2
## Salezio_3           Salezio  cream          3
## Estacao_1           Estacao  cream          1
## Estacao_2           Estacao  cream          2
## Estacao_3           Estacao  cream          3
## Videira_1           Videira  cream          1
## Videira_2           Videira  cream          2
## Videira_3           Videira  cream          3
## Rosada_1             Rosada    red          1
## Rosada_2             Rosada    red          2
## Rosada_3             Rosada    red          3

USING FULL UV-VISIBLE DATA (200-700 nm)

plot.spectra(uv.cassava.ds,"varieties")

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Data Pre-Processing

Smoothing and baseline correction

uv.cassava.wavelens = get.x.values.as.num(uv.cassava.ds)
x.axis.sm = seq(min(uv.cassava.wavelens), max(uv.cassava.wavelens),10)
uv.cassava.smooth = smoothing.interpolation(uv.cassava.ds, method = "loess", x.axis = x.axis.sm)
plot.spectra(uv.cassava.smooth, "varieties")

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uv.cassava.bg = data.correction(uv.cassava.smooth,"background")
uv.cassava.offset = data.correction(uv.cassava.bg, "offset")
uv.cassava.baseline = data.correction(uv.cassava.offset, "baseline")
sum.dataset(uv.cassava.baseline)
## Dataset summary:
## Valid dataset
## Description:  UV data for cassava-smoothed with hyperSpec spc.loess; background correction; offset correction; baseline correction 
## Type of data:  undefined 
## Number of samples:  30 
## Number of data points 51 
## Number of metadata variables:  3 
## Label of x-axis values:  wavelength(nm) 
## Label of data points:  absorbance 
## Number of missing values in data:  0 
## Mean of data values:  0.08889 
## Median of data values:  0.02441 
## Standard deviation:  0.1923 
## Range of values:  -0.0002181 1.29 
## Quantiles: 
##         0%        25%        50%        75%       100% 
## -0.0002181  0.0076378  0.0244131  0.0764408  1.2895338
plot.spectra(uv.cassava.baseline, "varieties")

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UNIVARIATE ANALYSIS

uv.cassava.baseline.anova = univariate.analysis(uv.cassava.baseline, type = "anova", "varieties")
uv.cassava.baseline.anova[1:10,]
##       pvalues  logs       fdr
## 500 1.073e-18 17.97 5.470e-17
## 470 4.485e-18 17.35 1.144e-16
## 490 1.394e-17 16.86 2.083e-16
## 460 1.642e-17 16.78 2.083e-16
## 510 2.043e-17 16.69 2.083e-16
## 480 6.334e-17 16.20 5.384e-16
## 290 7.563e-17 16.12 5.510e-16
## 440 3.299e-16 15.48 2.103e-15
## 450 4.829e-16 15.32 2.736e-15
## 300 1.322e-15 14.88 6.740e-15
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            tukey
## 500                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 470                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 490                                                                                                                                                                                                                                                                                                                                                                                                                                                   Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada; Videira-Salezio
## 460                                                                                                                                                                                                                                                                                                   Rosada-Amarela; Catarina-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 510                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 480                                                                                                                                                                                                                                                                                                                                                                                                                                                  Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Salezio-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 290                                    Apronta.mesa-Amarela; IAC.576.70-Amarela; Oriental-Amarela; Pioneira-Amarela; Salezio-Amarela; Videira-Amarela; Catarina-Apronta.mesa; Estacao-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Videira-Apronta.mesa; IAC.576.70-Catarina; Oriental-Catarina; Pioneira-Catarina; Salezio-Catarina; Videira-Catarina; IAC.576.70-Estacao; Oriental-Estacao; Pioneira-Estacao; Salezio-Estacao; Videira-Estacao; Pioneira-IAC.576.70; Rosada-IAC.576.70; Videira-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Videira-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 440                                                                                                                                                         Apronta.mesa-Amarela; Estacao-Amarela; Oriental-Amarela; Rosada-Amarela; Catarina-Apronta.mesa; IAC.576.70-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Estacao-Catarina; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; IAC.576.70-Estacao; Pioneira-Estacao; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 450                                                                                                                                                                               Oriental-Amarela; Rosada-Amarela; Salezio-Amarela; Catarina-Apronta.mesa; IAC.576.70-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Estacao-Catarina; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; IAC.576.70-Estacao; Pioneira-Estacao; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 300 Apronta.mesa-Amarela; IAC.576.70-Amarela; Oriental-Amarela; Pioneira-Amarela; Salezio-Amarela; Videira-Amarela; Catarina-Apronta.mesa; Estacao-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Videira-Apronta.mesa; IAC.576.70-Catarina; Oriental-Catarina; Pioneira-Catarina; Salezio-Catarina; Videira-Catarina; IAC.576.70-Estacao; Oriental-Estacao; Pioneira-Estacao; Salezio-Estacao; Videira-Estacao; Pioneira-IAC.576.70; Rosada-IAC.576.70; Videira-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Videira-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada; Videira-Salezio

Hierarchical Cluster Analysis

Using Euclidian Distance

uv.cassava.hc = clustering(uv.cassava.ds, method = "hc", distance = "euclidean")
dendrogram.plot(uv.cassava.ds, uv.cassava.hc)

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dendrogram.plot.col(uv.cassava.ds, uv.cassava.hc, "varieties")

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Principal Components Analysis

Importance of components: Proportion of Variance explained in each component

uv.cassava.pca = pca.analysis.dataset(uv.cassava.ds)
summary(uv.cassava.pca)
## Importance of components:
##                           PC1    PC2   PC3   PC4    PC5     PC6     PC7
## Standard deviation     17.129 10.087 8.289 4.249 3.0243 1.77968 1.31468
## Proportion of Variance  0.586  0.203 0.137 0.036 0.0183 0.00632 0.00345
## Cumulative Proportion   0.586  0.789 0.926 0.962 0.9802 0.98651 0.98996
##                            PC8     PC9    PC10    PC11    PC12    PC13
## Standard deviation     0.99687 0.84476 0.77221 0.72841 0.63003 0.47799
## Proportion of Variance 0.00198 0.00142 0.00119 0.00106 0.00079 0.00046
## Cumulative Proportion  0.99194 0.99337 0.99456 0.99562 0.99641 0.99686
##                           PC14    PC15    PC16    PC17    PC18    PC19
## Standard deviation     0.43455 0.41855 0.40385 0.36874 0.33393 0.32915
## Proportion of Variance 0.00038 0.00035 0.00033 0.00027 0.00022 0.00022
## Cumulative Proportion  0.99724 0.99759 0.99792 0.99819 0.99841 0.99863
##                           PC20   PC21    PC22    PC23    PC24    PC25
## Standard deviation     0.32294 0.3174 0.29612 0.28055 0.26165 0.25277
## Proportion of Variance 0.00021 0.0002 0.00018 0.00016 0.00014 0.00013
## Cumulative Proportion  0.99883 0.9990 0.99921 0.99937 0.99950 0.99963
##                           PC26   PC27    PC28    PC29     PC30
## Standard deviation     0.23929 0.2257 0.20367 0.18610 7.61e-15
## Proportion of Variance 0.00011 0.0001 0.00008 0.00007 0.00e+00
## Cumulative Proportion  0.99975 0.9999 0.99993 1.00000 1.00e+00

Robust and centralized pca (3D and 2D)

pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "varieties", ellipses=T)

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pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses=T, pallette=2)

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pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "varieties", ellipses="F")

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pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses="T", pallette=2, labels="T")

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pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses="T", pallette=2, labels="F")

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CAROTENOIDS FINGERPRINT REGION (400-500 nm)

Carotenoids have absorption maxima in the UV-visible region of 450 nm

uv.cassava.carot = subset.x.values.by.interval(uv.cassava.ds, min.value = 400, max.value = 500)
sum.dataset(uv.cassava.carot)
## Dataset summary:
## Valid dataset
## Description:  UV data for cassava 
## Type of data:  uvv-spectra 
## Number of samples:  30 
## Number of data points 101 
## Number of metadata variables:  3 
## Label of x-axis values:  wavelength(nm) 
## Label of data points:  absorbance 
## Number of missing values in data:  0 
## Mean of data values:  0.127 
## Median of data values:  0.02827 
## Standard deviation:  0.2668 
## Range of values:  -0.0228 1.498 
## Quantiles: 
##       0%      25%      50%      75%     100% 
## -0.02280  0.01033  0.02827  0.10888  1.49789

Plotting spectra

plot.spectra(uv.cassava.carot, "varieties", legend="topleft")

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Principal Components Analysis

Importance of components: Proportion of Variance explained in each component

uv.cassava.carot.pca = pca.analysis.dataset(uv.cassava.carot, scale = T, center = T)
summary(uv.cassava.carot.pca)
## Importance of components:
##                           PC1     PC2    PC3     PC4     PC5   PC6     PC7
## Standard deviation     10.012 0.86277 0.1417 0.05031 0.02480 0.014 0.00975
## Proportion of Variance  0.992 0.00737 0.0002 0.00003 0.00001 0.000 0.00000
## Cumulative Proportion   0.992 0.99977 1.0000 0.99999 1.00000 1.000 1.00000
##                            PC8     PC9    PC10    PC11  PC12    PC13
## Standard deviation     0.00782 0.00479 0.00384 0.00307 0.003 0.00221
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.000 0.00000
## Cumulative Proportion  1.00000 1.00000 1.00000 1.00000 1.000 1.00000
##                           PC14    PC15    PC16    PC17    PC18    PC19
## Standard deviation     0.00207 0.00197 0.00189 0.00175 0.00169 0.00158
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion  1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
##                          PC20    PC21    PC22    PC23    PC24    PC25
## Standard deviation     0.0015 0.00139 0.00133 0.00124 0.00116 0.00112
## Proportion of Variance 0.0000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion  1.0000 1.00000 1.00000 1.00000 1.00000 1.00000
##                          PC26     PC27     PC28     PC29     PC30
## Standard deviation     0.0011 0.000965 0.000929 0.000818 9.93e-16
## Proportion of Variance 0.0000 0.000000 0.000000 0.000000 0.00e+00
## Cumulative Proportion  1.0000 1.000000 1.000000 1.000000 1.00e+00

PCAs Graphics

pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "varieties", ellipses="F")

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pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "varieties", ellipses="T")

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pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "colors", labels="F", pallette=2, ellipses="T")

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Hierarchical Cluster Analysis

uv.cassava.carot.hc = clustering(uv.cassava.carot, method = "hc", distance = "euclidean")
dendrogram.plot(uv.cassava.carot, uv.cassava.carot.hc)

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dendrogram.plot.col(uv.cassava.carot, uv.cassava.hc, "colors")

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Profile and Quantification of Carotenoids using High Performance Liquid Chromatography (HPLC)

Subsequent analysis was performed to characterize the carotenoids by HPLC. The chromatographic analysis identified the cis-beta- and trans-beta-carotene, beta-carotene, lutein and beta-cryptoxanthin in all genotypes analyzed, confirmed the presence of lycopene only in Rosada genotype. Trans-beta-carotene was the major component in all samples.

setwd("/Users/Windows/Desktop/Miguel/Metabolomics-package")
load("hplcrodolfo.RData") 
hplcrodolfo
##       Cultivar Lutein ßCryptoxanthin aCarotene cisßcarotene Transßcarotene
## 1  Aprontamesa  0.091          0.109     0.000        0.000          0.000
## 2     Pioneira  0.319          0.071     0.306        2.967          3.425
## 3     Oriental  0.052          0.103     0.000        0.109          0.123
## 4      Amarela  0.685          0.033     0.043        3.292          4.224
## 5     Catarina  0.357          0.076     0.198        4.770          5.797
## 6     IAC57670  0.688          0.000     0.664        5.826          6.420
## 7      Salezio  0.055          0.066     0.328        0.065          0.354
## 8      Estacao  0.058          0.084     0.435        0.254          0.328
## 9      Videira  0.070          0.110     0.000        0.039          0.340
## 10      Rosada  0.511          0.605     4.732        4.480        166.296
##    Lycopene
## 1     0.000
## 2     0.000
## 3     0.000
## 4     0.000
## 5     0.000
## 6     0.000
## 7     0.000
## 8     0.000
## 9     0.000
## 10    1.534
cultivar=factor(hplcrodolfo$Cultivar)  
cultivar
##  [1] Aprontamesa Pioneira    Oriental    Amarela     Catarina   
##  [6] IAC57670    Salezio     Estacao     Videira     Rosada     
## 10 Levels: Amarela Aprontamesa Catarina Estacao IAC57670 ... Videira
hplc<-hplcrodolfo[2:7]  

Apply function of ade4

require(ade4)
## Loading required package: ade4
## Warning: package 'ade4' was built under R version 3.1.2
HPLC <- dudi.pca(hplc, center = TRUE, scale = TRUE, scan = F,nf=5)
summary(HPLC)   ##summarize the function
## Class: pca dudi
## Call: dudi.pca(df = hplc, center = TRUE, scale = TRUE, scannf = F, 
##     nf = 5)
## 
## Total inertia: 6
## 
## Eigenvalues:
##       Ax1       Ax2       Ax3       Ax4       Ax5 
## 4.2593319 1.6109364 0.1050266 0.0240342 0.0006663 
## 
## Projected inertia (%):
##      Ax1      Ax2      Ax3      Ax4      Ax5 
## 70.98887 26.84894  1.75044  0.40057  0.01111 
## 
## Cumulative projected inertia (%):
##     Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
##   70.99   97.84   99.59   99.99  100.00 
## 
## (Only 5 dimensions (out of 6) are shown)
HPLC$eig       ##eigenvalues (variability in the data)
## [1] 4.259e+00 1.611e+00 1.050e-01 2.403e-02 6.663e-04 4.587e-06
HPLC$li        ##row cordinates
##      Axis1   Axis2     Axis3     Axis4      Axis5
## 1   1.0560 -0.9772  0.140625 -0.118353  0.0217429
## 2   0.4634  0.5304 -0.188201 -0.015570  0.0249376
## 3   1.0947 -1.0398 -0.001810 -0.093596 -0.0219527
## 4   0.2746  1.6941  0.742037 -0.111167 -0.0098409
## 5   0.2188  1.1486 -0.653985 -0.205050 -0.0173457
## 6  -0.1705  2.4414 -0.090789  0.220190  0.0069017
## 7   1.0822 -1.0046  0.007296  0.233256 -0.0540229
## 8   0.9711 -0.9825 -0.052789  0.213374  0.0401974
## 9   1.0649 -1.0249  0.070085 -0.120416  0.0103131
## 10 -6.0551 -0.7856  0.027531 -0.002668 -0.0009305
HPLC$l1        ##row normed cordinates
##        RS1     RS2       RS3      RS4      RS5
## 1   0.5117 -0.7699  0.433923 -0.76342  0.84230
## 2   0.2246  0.4179 -0.580727 -0.10043  0.96606
## 3   0.5304 -0.8193 -0.005586 -0.60373 -0.85043
## 4   0.1330  1.3348  2.289686 -0.71707 -0.38123
## 5   0.1060  0.9050 -2.017986 -1.32265 -0.67196
## 6  -0.0826  1.9236 -0.280147  1.42031  0.26737
## 7   0.5244 -0.7915  0.022514  1.50459 -2.09281
## 8   0.4705 -0.7741 -0.162891  1.37634  1.55722
## 9   0.5160 -0.8075  0.216261 -0.77673  0.39952
## 10 -2.9340 -0.6189  0.084953 -0.01721 -0.03605
HPLC$co     ##column cordinates (correlations between variables and pcs)
##                  Comp1   Comp2    Comp3     Comp4      Comp5
## Lutein         -0.4767  0.8499  0.22431 -0.007895  0.0033189
## ßCryptoxanthin -0.9231 -0.3718 -0.00572 -0.096675  0.0147035
## aCarotene      -0.9830 -0.1375 -0.02382  0.119013  0.0105172
## cisßcarotene   -0.5326  0.8142 -0.23026 -0.018924 -0.0006946
## Transßcarotene -0.9867 -0.1608  0.01694 -0.008404 -0.0135522
## Lycopene       -0.9780 -0.2063  0.02832 -0.005738 -0.0120158
HPLC$c1    ##column normed scores (loadings)
##                    CS1     CS2      CS3      CS4      CS5
## Lutein         -0.2310  0.6697  0.69216 -0.05093  0.12857
## ßCryptoxanthin -0.4473 -0.2930 -0.01765 -0.62359  0.56960
## aCarotene      -0.4763 -0.1083 -0.07350  0.76768  0.40743
## cisßcarotene   -0.2581  0.6415 -0.71052 -0.12207 -0.02691
## Transßcarotene -0.4781 -0.1267  0.05226 -0.05421 -0.52500
## Lycopene       -0.4739 -0.1625  0.08738 -0.03701 -0.46548

Plot PCA

biplot(HPLC)